Fedmed-Gan: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

Xie, Guoyang; Wang, Jinbao; Huang, Yue; Lyu, Jiayi; Zheng, Feng; Zheng, Yefeng; Jin, Yaochu

doi:10.2139/ssrn.4342071

Cited by 6 publications

(5 citation statements)

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“…• C. Generator on server, GAN on clients: 5 papers [55,64,65,72,76] had a GAN on each client and a generator on the server. Usually, each client trains and sends generator parameters to the server, which aggregates them and sends them back, and so on.…”

Section: What Methods Have Been Used For Federated Synthesis?mentioning

confidence: 99%

Federated learning for generating synthetic data: a scoping review

Little,

Elliot,

Allmendinger

2023

IJPDS

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IntroductionFederated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format. ObjectivesThe objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps. MethodsA scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk. ResultsA total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data. ConclusionsFederated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.

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Section: What Methods Have Been Used For Federated Synthesis?mentioning

confidence: 99%

Federated learning for generating synthetic data: a scoping review

Little,

Elliot,

Allmendinger

2023

IJPDS

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“…We demonstrated the proposed approach against a centrally-trained translation model [8], and FL-based translation models including FedGAN [28], FedMRI [11] and FedMedGAN [37]. The centralized model and FedGAN was implemented with matching architecture to the proposed model, except for the AdaIN layers that were excluded.…”

Section: Competing Methodsmentioning

confidence: 99%

“…FedMedGAN was implemented with a U-Net backbone as originally proposed in [37]. However, the loss function of the proposed model was used in FedMedGAN as opposed to cycle-consistency loss for fair comparison in the paired translation tasks reported here.…”

Section: Competing Methodsmentioning

confidence: 99%

A Specificity-Preserving Generative Model for Federated MRI Translation

Dalmaz

Usama

Elmas

et al. 2022

Lecture Notes in Computer Science

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MRI translation models learn a mapping from an acquired source contrast to an unavailable target contrast. Collaboration between institutes is essential to train translation models that can generalize across diverse datasets. That said, aggregating all imaging data and training a centralized model poses privacy problems. Recently, federated learning (FL) has emerged as a collaboration framework that enables decentralized training to avoid sharing of imaging data. However, FLtrained translation models can deteriorate by the inherent heterogeneity in the distribution of MRI data. To improve reliability against domain shifts, here we introduce a novel specificity-preserving FL method for MRI contrast translation. The proposed approach is based on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.

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“…On the other hand, the unique information from different MRI modalities complements each other [15]. Consequently, there has been a growing interest in multi-modal MRI image synthesis, which aims to enhance the quality of synthetic images and improve diagnostic utility [16][17][18][19][20][21]. Recent advancements have introduced methods that enable training with unregistered or unpaired data.…”

Section: Introductionmentioning

confidence: 99%

Assessing the efficacy of 3D Dual-CycleGAN model for multi-contrast MRI synthesis

Mahboubisarighieh,

Shahverdi,

Jafarpoor Nesheli

et al. 2024

Egypt J Radiol Nucl Med

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Background This research presents a novel methodology for synthesizing 3D multi-contrast MRI images utilizing the 3D Dual-CycleGAN architecture. The performance of the model is evaluated on different MRI sequences, including T1-weighted (T1W), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), and FLAIR sequences. Results Our approach demonstrates proficient learning capabilities in transforming T1W images into target modalities. The proposed framework encompasses a combination of different loss functions including voxel-wise, gradient difference, perceptual, and structural similarity losses. These loss components, along with adversarial and dual cycle-consistency losses, contribute significantly to realistic and accurate syntheses. Evaluation metrics including MAE, PMAE, RMSE, PCC, PSNR, and SSIM are employed to assess the fidelity of synthesized images compared to their ground truth counterparts. Empirical results indicate the effectiveness of the 3D Dual-CycleGAN model in generating T1c images from T1W inputs with minimal average discrepancies (MAE of 2.8 ± 2.61) and strong similarity (SSIM of 0.82 ± 0.28). Furthermore, the synthesis of T2W and FLAIR images yields promising outcomes, demonstrating acceptable average discrepancies (MAE of 3.87 ± 3.32 for T2W and 3.82 ± 3.32 for FLAIR) and reasonable similarities (SSIM of 0.82 ± 0.28 for T2W and 0.80 ± 0.29 for FLAIR) relative to the original images. Conclusions These findings underscore the efficacy of the 3D Dual-CycleGAN model in generating high-fidelity images, with significant implications for diverse applications in the field of medical imaging.

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Fedmed-Gan: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

Cited by 6 publications

References 0 publications

Federated learning for generating synthetic data: a scoping review

Federated learning for generating synthetic data: a scoping review

A Specificity-Preserving Generative Model for Federated MRI Translation

Assessing the efficacy of 3D Dual-CycleGAN model for multi-contrast MRI synthesis

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